Introduction to Geospatial Information Management

and Spatial Databases

Lecture 4

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Value of SDBMS Traditional (non-spatial) database management systems provide:

Persistence across failures.

Allows concurrent access to data.

Scalability to search queries on very large datasets which do not fit inside main memories of computers.

Efficient for non-spatial queries, but not for spatial queries.

Non-spatial queries:

List the names of all bookstore with more than ten thousand titles.

List the names of ten customers, in terms of sales, in the year 2001.

Spatial Queries:

List the names of all bookstores with 10 km of Chiayi city.

List all customers who live in Taipei city and its adjoining counties. 2

Value of SDBMS – Spatial Data Examples

Examples of non-spatial data

Names, phone numbers, email addresses of people

Examples of Spatial data

Census data

NASA satellites imagery - terabytes of data per day

Weather and Climate Data

Rivers, farms, ecological impact

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Value of SDBMS – Users, Application Domains

Many important application domains have spatial data and queries.

Army Field Commander: Has there been any significant enemy troop movement since last night?

Insurance Risk Manager: Which homes are most likely to be affected in the next great flood on the Changhua county?

Medical Doctor: Based on this patient's MRI, have we treated somebody with a similar condition ?

Molecular Biologist: Is the topology of the amino acid biosynthesis gene in the genome found in any other sequence feature map in the database ?

Astronomer: Find all blue galaxies within 2 arcmin of quasars.

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Value of SDBMS – Users, Application Domains

Various fields/applications require management of geometric, geographic or spatial data:

A geographic space: surface of the earth

Man-made space: layout of VLSI design

Model of a rat brain

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What is a SDBMS A SDBMS is a software module that

can work with an underlying DBMS (e.g., MySQL, GIS and Spatial Extentions)

supports spatial data models, spatial abstract data types (ADTs) and a query language from which these ADTs are callable

supports spatial indexing, efficient algorithms for processing spatial operations, and domain specific rules for query optimization

Example: Oracle Spatial data cartridge, ESRI SDE

can work with Oracle 8i DBMS

Has spatial data types (e.g. polygon), operations (e.g. overlap) callable from SQL3 query language

Has spatial indices, e.g. R-trees

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What is a SDBMS Common challenge: dealing with large collections of relatively simple

geometric objects. (e.g., rectangle, point, polygon)

Different from image and pictorial database systems:

Containing sets of objects in space rather than images or pictures of a space

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SDBMS Example Consider a spatial dataset with:

County boundary (dashed white line)

Census block - name, area, population, boundary (dark line)

Water bodies (dark polygons)

Satellite Imagery (gray scale pixels)

Storage in a SDBMS table:

create table census_blocks (

name string, area float, population number, boundary polygon );

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Modeling Spatial Data in Traditional DBMS

A row in the table census_blocks

Question: Is polyline datatype supported in DBMS?

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Spatial Data Types and Traditional Databases

Traditional relational DBMS

Support simple data types, e.g. number, strings, date

Modeling spatial data types is tedious

Example: next slide shows modeling of polygon using (numbers)

Three new tables: polygon, edge, points.

Note: Polygon is a polyline where last point and first point are same

A simple unit square represented as 16 rows across 3 tables

Simple spatial operators, e.g. area(), require joining tables

Tedious and computationally inefficient

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Mapping “census_table” into a Relational Database

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Evolution of DBMS technology

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Spatial Data Types and Post-relational Databases

Post-relational DBMS

Support user defined abstract data types

Spatial data types (e.g. polygon) can be added

Choice of post-relational DBMS

Object oriented (OO) DBMS

Object relational (OR) DBMS

A spatial database is a collection of (spatial data types), (operators), (indices), processing strategies, etc. and can work with many post-relational DBMS as well as programming languages like Java, Visual Basic etc.

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GIS is a (software ) to visualize and analyze spatial data using spatial analysis functions such as

Search Thematic search, search by region, (re-)classification

Location analysis Buffer, corridor, overlay

Terrain analysis Slope/aspect, catchment, drainage network

Flow analysis Connectivity, shortest path

Distribution Change detection, proximity, nearest neighbor

Spatial analysis/Statistics Pattern, centrality, autocorrelation, indices of similarity, topology: hole description

Measurements Distance, perimeter, shape, adjacency, direction

GIS uses SDBMS

to store, search, query, share large spatial data sets

How is a SDBMS different from a GIS ?

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SDBMS focuses on

(Efficient storage), (querying), sharing of large spatial datasets

Provides simpler set based query operations

Example operations: search by region, overlay, nearest neighbor, distance, adjacency, perimeter etc.

Uses (spatial indices) and (query optimization) to speedup queries over large spatial datasets.

SDBMS may be used by applications other than GIS

Astronomy, Genomics, Multimedia information systems, ...

Will one use a GIS or a SDBM to answer the following:

How many neighboring countries does USA have?

Which country has highest number of neighbors?

How is a SDBMS different from a GIS ?

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Three meanings of the acronym GIS Geographic Information Services

Web-sites and service centers for casual users, e.g. travelers

Example: Service (e.g. AAA, mapquest) for route planning

Geographic Information Systems

Software for professional users, e.g. cartographers

Example: ESRI Arc/View software

Geographic Information Science

Concepts, frameworks, theories to formalize use and development of geographic information systems and services

Example: design spatial data types and operations for querying

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Components of a SDBMS Recall: a SDBMS is a software module that

can work with an underlying DBMS

supports spatial data models, spatial ADTs and a query language from which these ADTs are callable

supports spatial indexing, algorithms for processing spatial operations, and domain specific rules for query optimization

Components include

spatial data model, query language, query processing, file organization and indices, query optimization, etc.

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Spatial Taxonomy, Data Models Spatial Taxonomy:

multitude of descriptions available to organize space.

Topology models homeo-morphic relationships, e.g. overlap

Euclidean space models distance and direction in a plane

Graphs models connectivity, Shortest-Path

Spatial data models

rules to identify identifiable objects and properties of space

Object model helps manage identifiable things, e.g. mountains, cities, land-parcels etc.

Field model helps manage continuous and amorphous phenomenon, e.g. wetlands, satellite imagery, snowfall etc.

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A collection of concepts to describe to describe:

structure of a database

data relationships

data semantics

data constraints

Data Model Operations: operations for specifying database retrievals and updates.

Data Models

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Modeling* Without lose of generality, assume 2-D and GIS application, two basic

things need to be represented:

Objects in space: cities, forests, or rivers

modeling single objects

Space: say something about every point in space (e.g., partition of a country into districts)

modeling spatially related collections of objects

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Modeling* Fundamental abstractions for modeling single

objects:

Point: object represented only by its location in space, e.g., center of a state

Line (actually a curve or ployline): representation of moving through or connections in space, e.g., road, river

Region: representation of an extent in 2-D space, e.g., lake, city

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Modeling* Instances of spatially related collections of

objects:

Partition: set of region objects that are required to be disjoint (adjacency or region objects with common boundaries), e.g., thematic maps

Networks: embedded graph in plane consisting of set of points (vertices) and lines (edges) objects, e.g. highways, power supply lines, rivers

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Modeling* Spatial relationships

Topological relationships: e.g., adjacent, inside, disjoint.

Direction relationships: e.g., above, below, or north_of, southwest_of, …

Metric relationships: e.g., distance

There are 6 valid possible topological relationships between two simple regions (no holes, connected):

disjoint, in, touch, equal, cover, overlap

A

B

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Modeling* SDBMS data model must be extended by ADTs at the level of atomic

data types (such as integer, string), or better be open for user-defined types (OR-DBMS approach):

relation states (sname: STRING; area: REGION; spop: INTEGER)

relation cities (cname: STRING; center: POINT; ext: REGION; cpop: INTEGER);

relation rivers (rname: STRING; route: LINE)

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Spatial Query Language Spatial query language

Spatial data types, e.g. point, linestring, polygon, …

Spatial operations, e.g. overlap, distance, nearest neighbor, …

Callable from a query language (e.g. SQL3) of underlying DBMSSELECT S.nameFROM Senator SWHERE S.district.Area() < 300

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Query Processing Efficient algorithms to answer spatial queries

Common Strategy – (filter) and (refine)

Filter Step:Query Region overlaps with MBRs of B,C and D

Refine Step: Query Region overlaps with B and C

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Querying* …Fundamental spatial algebra operations:

Spatial selection: returning those objects satisfying a spatial predicate with the query object

“All cities in Taiwan”SELECT sname FROM cities c WHERE c.center inside Taiwan.area

“All rivers intersecting a query window” SELECT * FROM rivers r WHERE r.route intersects Window

“All big cities no more than 50 Kms from Taichung” SELECT cname FROM cities c

WHERE dist(c.center,Taichung.center) < 100 and c.pop > 500k (conjunction with other predicates and query optimization)

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Querying* … Spatial join: A join which compares any two joined objects based on a

predicate on their spatial attribute values.

“For each river pass through Taichung, find all cities within less than 50 Kms.”

SELECT r.rname, c.cname,FROM rivers r, cities cWHERE r.route intersects Taichung.area

anddist(r.route,c.area) < 50 Km

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File Organization and Indices A difference between GIS and SDBMS assumptions

GIS algorithms: dataset is loaded in main memory (a)

SDBMS: dataset is on secondary storage e.g disk (b)

SDBMS uses space filling curves and spatial indices to efficiently search disk resident large spatial datasets

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Organizing spatial data with space filling curves

Issues:

Sorting is not naturally defined on spatial data

Many efficient search methods are based on sorting datasets

Space filling curves

Impose an ordering on the locations in a multi-dimensional space

Examples: row-order, z-order, Hilbert curve (higher spatial correlation)

Allow use of traditional efficient search methods on spatial data

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Spatial Indexing To expedite spatial selection (as well as other operations such as spatial

joins, …)

It organizes space and the objects in it in some way so that only parts of the space and a subset of the objects need to be considered to answer a query.

Two main approaches:

Dedicated spatial data structures (e.g., R-tree)

Spatial objects mapped to a 1-D space to utilize standard indexing techniques (e.g., B-tree)

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Summary SDBMS is valuable to many important applications

SDBMS is a software module

works with an underlying DBMS

provides spatial ADTs callable from a query language

provides methods for efficient processing of spatial queries

Components of SDBMS include

spatial data model, spatial data types and operators,

spatial query language, processing and optimization

spatial data mining

SDBMS is used to store, query and share spatial data for GIS as well as other applications

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